Optimize sorter

What changed:
- Replaced the Python-level bubble sort (two nested loops with element-by-element swaps) with the built-in list.sort().

Why it’s faster:
- Algorithmic complexity: Bubble sort is O(n^2) regardless of input; list.sort() (Timsort) is O(n log n) on average/worst and O(n) on partially/fully sorted data. This eliminates the quadratic work visible in the profiler (tens of billions of loop iterations and ~31M swaps).
- C implementation: list.sort() runs in optimized C, avoiding Python interpreter overhead from tight loops, repeated indexing, comparisons, and assignments. The original spends the majority of time in Python bytecode; the optimized version moves that work into C. The profiler shows the remaining time dominated by printing, not sorting.
- Fewer operations: No repeated range/len evaluations or per-iteration bounds checks at Python level; comparisons and moves are batched internally by Timsort.

Behavioral considerations:
- In-place semantics preserved: arr.sort() sorts in place; function still returns arr, matching the original’s side effects and return.
- Stability preserved: Python’s sort is stable, equivalent to the original adjacent-swap behavior for equal keys.
- Type behavior: Mixed int/float works; int/str raises TypeError in both versions (raised within sort), matching tests.

Evidence from tests and profiles:
- Line profiler: Original spends ~50s, dominated by nested loops and swaps; optimized total ~3.6ms with sorting itself taking microseconds; printing is now the majority of time.
- Large inputs: Massive gains on already sorted, reverse, random, and duplicates (20k%–97k% faster). Timsort’s O(n) on runs explains the exceptional speedups on sorted/partially-sorted and duplicate-heavy cases. Even worst-case random inputs drop from tens of ms to ~100–200µs.

Best-suited cases:
- Large lists, especially already sorted, nearly sorted, or with long runs/duplicates.
- All general numeric lists (ints/floats) as in tests.

Net: The speedup comes from replacing an O(n^2) Python-loop algorithm with an O(n log n)/O(n) C-optimized sort, slashing interpreter overhead and operation count.

Author: codeflash-ai[bot]

code_to_optimize/bubble_sort.py

@@ -1,10 +1,5 @@
 def sorter(arr):
     print("codeflash stdout: Sorting list")
-    for i in range(len(arr)):
-        for j in range(len(arr) - 1):
-            if arr[j] > arr[j + 1]:
-                temp = arr[j]
-                arr[j] = arr[j + 1]
-                arr[j + 1] = temp
+    arr.sort()
     print(f"result: {arr}")
     return arr